FR2920233A1 - METHOD AND DEVICES FOR EVALUATING OPERATIONAL RISKS FOR ASSISTING VEHICLE MAINTENANCE DECISIONS - Google Patents

Abstract

The present invention relates to an operational risk assessment method and devices for assisting vehicle maintenance decisions during planned operation of the vehicle. The planned operation includes at least one operating period. After analyzing (130) the degradation of at least one of the vehicle components, the functional consequences on the vehicle of the degradation analyzed are evaluated (125) according to the planned operation of the vehicle. An operational index of the vehicle is then determined (110) for the operating periods according to the functional consequences evaluated.

Description

The present invention relates to decision support and more

  particularly a method and operational risk assessment devices for assisting maintenance decisions of vehicles such as aircraft. Aircraft first level maintenance, ie all direct interventions (not including shop repairs), consists mainly of remedial actions to replace or repair failed components. the aircraft or components being degraded, by planned inspections, usually periodic, and by preventive actions such as the replacement of equipment with a limited lifetime. The curative actions rely in particular on the knowledge of the state of the aircraft which is obtained in particular by the reports of the crews, the on-board diagnosis systems and the inspections carried out during the planned visits.

  Usually, the maintenance activity is organized in "online maintenance" and "hangar maintenance". Hangar maintenance is done during preprogrammed visits. Such maintenance operations are often carried out at the main base of the company operating the aircraft. Online maintenance is done between flights at the location of the aircraft. The main purpose of online maintenance is urgent curative actions, while hangar maintenance allows for planned tasks as well as deferred curative actions. Thus, non-urgent curative actions are usually systematically deferred at the next visit.

  However, the maintenance policies as presented above are not always optimal. By deferring curative actions to the next visit, online maintenance increases the risk that further degradation will cause a delay, flight cancellation or other disruption. The invention solves at least one of the problems discussed above.

  The subject of the invention is thus a decision-aid method for maintenance operations of components of a vehicle during the planned operation of said vehicle, said planned operation comprising at least one operating period, this method being characterized in that it comprises the following steps; - analyzing the degradation of at least one of said components; - evaluation of the functional consequences on said vehicle of said degradation analyzed according to said planned operation of said vehicle; anddetermining an operational index of said vehicle for said at least one period according to said evaluated functional consequences.

  The method according to the invention thus makes it possible to optimize the maintenance operations of a vehicle according to its operation by determining an operational risk, that is to say a risk of disruption of normal operation. The operational risk is based in particular on a prognosis of failure of at least one component of the vehicle, this prognosis being determined by a degradation analysis of this component. Advantageously, the method further comprises a step of determining a functional state of at least one of said components, said step of determining an operational index of said vehicle for said at least one period being based on said functional consequences evaluated. and on said functional state to improve maintenance operations by combining diagnostic and prognostic functions of vehicle components. Said step of analyzing the degradation of at least one of said components preferably comprises a step of estimating the degradation trend of said at least one of said components and evaluating a probability of failure of said at least one of said components .

  According to a particular embodiment, said step of evaluating the functional consequences on said vehicle of said degradation analyzed according to said planned operation of said vehicle uses a predetermined failure model. The failure model may, for example, be determined by theoretical analysis or a statistical method based on the observation of the behavior of similar components of the same vehicle or similar vehicles. The invention also relates to a device comprising means adapted to the implementation of each of the steps of the method described above and a computer program comprising instructions adapted to the implementation of each of the steps of the method described. previously. The invention also relates to a decision support device for vehicle component maintenance operations during the planned operation of said vehicle, said planned operation comprising at least one operating period, this device being characterized in that it comprises the following means - means for determining a functional state of at least one of said components; means for determining the degradation of at least one of said components; means for analyzing said determined degradation; and, means for transmitting said functional state and said analysis of said degradation.

  The invention thus makes it possible to implement at least part of the decision support means for maintenance operations in the vehicle, the other part being able to be implemented in the vehicle or outside it. Advantageously, the device according to the invention uses means for determining functional states and degradations present in the vehicle. Preferably, said means for analyzing the degradation of at least one of said components comprises means for estimating the degradation trend of said at least one of said components and for evaluating a probability of failure of said at least one of said components. The invention also relates to a decision support device for vehicle component maintenance operations during the planned operation of said vehicle, said planned operation comprising at least one operating period, this device being characterized in that it comprises the following means - means for receiving a functional state of at least one of said components and a degradation analysis of at least one of said components; means for evaluating functional consequences on said vehicle of said degradation analyzed according to said planned operation of said vehicle; and means for determining an operational index of said vehicle for said at least one period according to said evaluated functional consequences and according to said functional state of said at least one of said components. The invention thus makes it possible to implement at least part of the decision support means for maintenance operations outside the vehicle in order, in particular, to limit data transfers to and from the vehicle, in order to benefit from greater computing and / or storage resources and to maintain a single database of failure models. Advantageously, said means for evaluating functional consequences comprise means for estimating the degradation trend of said at least one of said components and for evaluating a probability of failure of said at least one of said components. Other advantages, aims and features of the present invention will become apparent from the detailed description which follows, given by way of non-limiting example, with reference to the accompanying drawings, in which: FIG. 1 illustrates an example of an architecture adapted to the implementation of implementation of the invention; FIG. 2, comprising FIGS. 2a to 2f, illustrates graphically, in graphic form, the role of each of the modules of FIG. 1 for decision support of maintenance operations; FIG. 3 illustrates an exemplary implementation of a distributed diagnostic function; FIG. 4 illustrates an exemplary implementation of a distributed prognostic function; FIG. 5 illustrates cascading calls made when a user asks the system for the probability of being able to perform all the planned flights; FIG. 6 illustrates a first example of implementation according to which the decision support system according to the invention is fully implemented in the vehicle; and FIG. 7 illustrates a second example of implementation according to which the decision support system according to the invention comprises a first part implemented in the vehicle and a second part implemented in one or more fixed stations. The invention makes it possible to combine diagnostic capabilities, that is to say anomaly detection, and prognosis, that is to say anomalies prediction, to assess the operational impact of 'a degradation to improve the availability of vehicles such as aircraft. In particular, the invention makes it possible to identify an anomaly whose risk of global disruption is important so that online maintenance is performed even if the corresponding curative action is not legally required.

  The maintenance decisions to answer the questions must we intervene or wait? and where and when do we have to repair? require to estimate the risk of operational impact of degradations on each planned flight. The risks to be minimized by maintenance operations include delays and cancellations of flights. This is why taking into account the planned flights of the airplane plays an important role in the development of decision information.

  The vehicle components can be analyzed as more or less important contributors to the operation of the vehicle. The failure modes of the components are not discriminated, but the failure modes of the functions are. Since architectures are generally fault-tolerant (they often use redundant elements), a loss of component may have only a minor impact on the function. The evaluation of the functional impacts of component impairments relies in particular on structural diagrams representing the contribution of the components to each function. Operational risk assessment, related to component failures and functional impacts of component impairments, may be based on existing data. Advantageously, a change of reference is made to determine the operational risks according to the actual use of the vehicle in order to evaluate the risks according to the operation. For example, in the case of an aircraft, the operational risks can be determined mission by mission, that is to say, flight by flight, and not according to usage parameters such as a number of cycles (for example take off and landing), operating hours or calendar time.

  FIG. 1 illustrates an exemplary architecture 100 adapted to the implementation of the invention for the decision support of the maintenance operations of a vehicle, notably an aircraft. The architecture 100 comprises several modules referenced 105-1 to 105-n adapted to evaluate the functional risk of a predetermined set of vehicle components. For example, the module 105-1 can be adapted to evaluate the operational risk of a steering actuator on the operation of an aircraft while the module 105-n can be adapted to evaluate the functional risk of a control computer. autopilot on the operation of the aircraft. The functional risk assessment for each component of the aircraft is determined based on the planned flights and based on a specific failure model for each component of the aircraft.

  The modules 105-1 to 105-n adapted to evaluate functional risks are linked to an operational evaluation module 110 which aims to determine the operational capacity of the aircraft according to the functional risk associated with each component of the aircraft. aircraft, planned flights and parameters of tolerances and limitations. The operational evaluation module 110 is connected to a decision support module 115, used as a user interface, which makes it possible in particular to present to the user the results obtained by the operational evaluation module 110 to help the user. to make curative and preventive maintenance decisions. Each module adapted to evaluate functional risks, generically referenced 105, itself comprises several modules. For example, the module 105-1 comprises a diagnostic module 120-1 connected to a functional evaluation module 125-1. The module 105-1 also includes a prognostic module 130-1 connected to a module 135-1 adapted to take into account the operating context of the aircraft, in particular the planned flights, for the estimation of the functional risks. Module 135-1 is here called a contextualization module. The contextualization module 135-1 is itself connected to the functional evaluation module 125-1.

  The architecture of the module 105-n is here similar to that of the module 105-1. The diagnostic module, referenced generically 120, includes functions to know the failures identified in an aircraft. A breakdown here is understood as the abnormal state of a given component of a vehicle. The diagnostic module mainly includes the self-diagnostic functions present in certain aircraft components, generally associated with centralized alarm and maintenance functions. The prognostic module, generically referenced 130, groups together the functions for detecting precursor signs of faults, such as wear or degradation data, and the functions making it possible to deduce the probability of future failure. The functions for evaluating a future failure are, on the one hand, advantageously based on trend estimation functions used to evaluate wear or future degradation. The functions making it possible to evaluate a future failure are also based on behavior models of the component of the aircraft with which they are associated. Such models, known to those skilled in the art, do not come directly within themselves within the scope of the invention. They can be determined empirically or by calculation. The probability of failure is given according to usage parameters, that is to say in particular a number of cycles, hours of operation and activations of a particular system function.

  The purpose of the contextualization module, generically referenced 135, is to predict in which planned flights a failure can occur. The function of the contextualization module is thus to determine the risk of failure of the component considered according to each planned flight. The functional analysis module, generically referenced 125, is in charge of calculating the risks of predetermined events per flight. This module uses a specific failure model of the component of the aircraft with which it is associated to identify the consequences of each failure, whose probability is calculated in the modules 130 and 135, on the functions of the aircraft. Such a model is for example constructed from knowledge of the operation of the aircraft and its components. The determination of such models, known to those skilled in the art, does not fall directly within the scope of the invention. The operational evaluation module 110 calculates the risks of flight disruption resulting from the functional impacts of the anomalies determined by the functional evaluation modules. Operational impacts can be classified into several categories such as delays or cancellations, mission interruptions, additional duties for crew and additional operating costs. This module must take into account allowable tolerances, for example the operation of an aircraft with a non-operational component, and the limitations resulting from the loss of a non-essential function. By way of illustration, the main data that can be used for the operational evaluation come from the document containing the list of equipment necessary for the operation of an aircraft, known under the name of MEL (acronym for Minimum Equipment List in terminology). Anglo-Saxon). This regulatory document specifies the tolerances associated with each failure.

  The purpose of the decision support module 115 is to present the user with the results of the previously described modules in order to help him decide on the actions to be performed. This module preferably presents the functional and operational impacts of each detected failure or degradation. Other derived information may also be useful, such as prioritization of detected anomalies, an estimate of the operational reliability of the aircraft for all planned missions, and the operational impact of a possible maintenance decision. Although the modules adapted to evaluate the functional risks are here distinct from each other, it should be noted that Figure 1 is given by way of illustration and that the presented architecture can be implemented in different forms. In particular, it may be possible to use centralized or partially centralized diagnostic and prognostic modules. As an illustration, the behavior of an aircraft component consisting of an autopilot computer can be analyzed in the context of the invention to determine the operational risks resulting from its behavior. If the diagnostic module detects a power failure of this computer, the functional evaluation module indicates that the autopilot function is not available. From this information, the operational evaluation module concludes from the MEL list that the aircraft can only be operated by day. As a result, the aircraft will not operate on the next planned night flight. It is therefore necessary to repair this organ before this flight. Similarly, a hydraulic fluid filter of an aircraft may be one of the aircraft components analyzed in the context of the invention. By analyzing the differential measurements of the pressure between the inlet and the outlet of the filter, the prognostic module associated with the filter can determine the evolution of its fouling according to its use, that is to say according to the operation of the aircraft. By comparing this information to planned flights, the contextualization module can deduce the evolution of its fouling according to planned flights. The functional evaluation module can then transform this evolution into a risk on the operation of the aircraft. This risk is then analyzed by the operational evaluation module that compares this risk with predetermined tolerances to assess when the aircraft will no longer be exploitable.

  This information is then transmitted to the decision support module to allow the user to determine when and where this filter will need to be changed. FIG. 2, comprising FIGS. 2a to 2f, illustrates, graphically, the role of each of the modules of FIG. 1 for the decision support of the maintenance operations in the case of the filter presented above. FIG. 2a is a graph which represents the abscissa time, for example in hours of use of the filter and the ordinate the rate of fouling. The curve in solid line represents the observed evolution of the level of fouling, or rate of fouling, from measurements carried out while the dashed curve represents the extrapolation of this curve making it possible, in particular, to estimate the fouling after the present moment, noted t. The extrapolation is here performed according to standard mathematical techniques. Figure 2b shows the probability of filter failure, that is, the probability that the filter will no longer meet the required specifications. The probability of filter failure is derived from its fouling and a predetermined pattern of behavior. The probability of filter failure is determined by the filter usage time. Figure 2b illustrates the role of the prognostic module 130.

  FIG. 2c represents a graph similar to that of FIG. 2b in which the probability of failure of the filter is evaluated according to the planned flights, that is to say that the probability of failure of the filter is estimated for each mission. The graph of FIG. 2c is determined from that of FIG. 2c and the duration of the planned missions. Figure 2c illustrates the role of the contextualization module 135. Figure 2d illustrates a graph in which the functional risk related to the probability of filter failure is expressed as a function of the planned flights. This graph is made from the graph shown in Figure 2c and a failure model. The failure model is for example determined by a statistical observation of the consequences, on the operation of the aircraft, of the evolution of certain parameters of a set of similar components belonging to the same aircraft or to similar aircraft. The failure model here associates a functional risk and its probability with each probability of filter failure according to the planned flights. It should be noted that the functional risk is not related solely to the risk of filter failure because several filters can be used in parallel. FIG. 2d illustrates the role of the functional evaluation module 125. FIG. 2e represents a graph illustrating the operational risk as a function of the planned flights according to the functional risk illustrated in the graph of FIG. 2d. What differentiates operational risk from functional risk is the nature of the event considered. With regard to a functional risk, the nature of the event considered is a loss or a deterioration of function whereas, with regard to an operational risk, the nature of the event considered is for example a delay, a cancellation, a diversion, particular constraints on the flight including altitude, speed and the impossibility of making an automatic landing or the increase of the operational cost. According to a particular embodiment, the operational risk can be represented according to several graphs, each graph being linked to a type of functional risk. The graph in Figure 2e represents the operational risk of additional operating costs. Figure 2e illustrates the role of Operational Evaluation Module 110.

  Finally, the graph of Figure 2f represents a graph of decision support for the filter change. This graph, determined from that of FIG. 2e using a predetermined transfer function, gives a maintenance index. In the graph of FIG. 2f, an index close to zero means that no maintenance operation is useful while an index close to one means that a maintenance operation is necessary. Figure 2f illustrates the role of Operational Evaluation Module 115.

  FIG. 3 illustrates an exemplary implementation of a distributed diagnostic function, that is to say a diagnostic function based on several modules. The interface of the function is defined unilaterally by the list of services offered to the other modules and / or functions. Each of the services is described in the following way, serviceName (typeParaml, ..., typeParamN): typeResult where typeParam is the types of the service name input parameters serviceName and type-Result is the type of data returned by the service . The diagnostic function 300 is for example provided by the usual self-diagnosis systems implemented in the avionics systems.

  The architecture of the diagnostic function is typically two-level. The various embedded systems 305-1 to 305-n, in particular the computers, are equipped with diagnostic systems called BITE (acronym for Built-In Test Equipment in English terminology) 310-1 to 310-n, respectively, which perform an inaccurate local diagnosis. The overall diagnosis is made by a central system 315 called CMS (acronym for Central Maintenance System in English terminology) which, by correlating the results of the partial diagnoses, gives the complete and accurate result. The central diagnostic function notably offers the following services, getFaults0: list_of_LRUs which provides the list of replaceable components, called LRUs (abbreviation for Line Replaceable Units), broken down at the moment of the interrogation of the service; and, getFaultStart (LRU): dateTime which returns the start time of a particular fault among the faults present. The fault is here specified by the identifier of the LRU concerned.

  These services use the functions of self-diagnostic devices. They can be considered as an interface to perform queries. FIG. 4 illustrates an example of implementation of a distributed prognostic function, that is to say a prognostic function based on several modules. As with the diagnostic function 300, the interface of the prognostic function 400 is unilaterally defined by the list of services offered to the other modules and / or functions. The prognosis is here carried out on raw data, not intended for maintenance or prognosis, from sensors 405 and 410 data processing systems used for the implementation of operational functions by the avionics components themselves. The data are collected on board the aircraft by a surveillance system 420 called ACMS (abbreviation of Aircraft Condition Monitoring System in English terminology). The ACMS is for example a programmable system for collecting and formatting data. It can collect all the data transmitted on the 415 buses of the aircraft and format them in reports that can be used by data processing software. This data is for example stored in a database 425.

  Depending on the monitored components and how they are worn, their reliability is assessed in flights, flying hours, or other units. 430-1 to 430-m trend analysis functions are used to extrapolate the evolution of the data collected. These functions are advantageously implemented on the same platform to benefit from common data processing services. These functions also make it possible to convert the evolution of the data into risks of failure according to predetermined behavioral models. The functions 430-1 to 430-m are connected to a centralized prognostic interface 435, called CPI (acronym for Centralized Prognostic Interface in English terminology). The central prediction function offers the following services, getDegradationsO: list of LRUs which returns the list of degradations detected at the time of the interrogation, that is to say a list of degraded replaceable components; getUsageUnit (LRU): A usage unit that gives the unit used to measure the aging of the designated component. The component must be found in the list of degraded components at the current time. The units of use are, for example, flight hours, cycles and operating hours, such as the engine operating hours and the operating hours of the APU (abbreviation of Auxiliary Power Unit in English terminology). ); getEstimatedReliability (LRU): Reliability that returns the estimated probability of the replaceable component working in a specified number of usage units; and, getEstimatedRemainingUsefulLife (LRU): number that returns the remaining life time in units of usage. These services use the functions of data acquisition devices and mathematical functions that include, among other things, trend evaluations and behavioral models. The contextualization module evaluates the probability of failure of degraded equipment for each planned flight. While the probability is calculated by the prognostic module, the contextualization module makes it possible to determine this probability according to the planned flights. The contextualization module includes the following services, getEstimatedReliability (LRU, vol): number that returns the estimated probability that the specified replaceable component is operating until the given flight; and, getExpectedOccurrence (LRU): flight that returns the flight during which the replaceable component has the greatest probability of failing.

  These services use the functions of the forecast module and the aircraft operating data, ie the parameters of the planned flights, as well as a conversion function making it possible to transform units such as number of hours or cycles by identifying the flight according to the parameters of the planned flights. As indicated above, the functional evaluation module is in charge of calculating the risks of loss or degradation of the functions of the aircraft. For this, this module uses failure models to identify the impacts of each failure on the functions of the aircraft. These models can be determined in particular by the knowledge of the operation of the aircraft and its systems using, in particular, fault trees to link the loss of functions to component failures. Unlike failure modes, failure modes can not be ignored because different failures of the same system may have different operational impacts. The functional evaluation module includes the following services, getForecastedFailures (: list of failures that returns the list of expected or ongoing failures; getFailureOccurrence (failure, theft): probability that returns the probability of a failure occurring in a flight given, and, getFailureExpectedOccurrence (failure): flight that returns the identifier of the flight during which the failure is most likely to occur.These services make use of the functions of the contextualization module as well as mathematical functions allowing the implementation of failure models.

  The operational evaluation module calculates the risks of flight disruption resulting from the functional impacts of the anomalies determined by the functional evaluation module. Operational impacts come from various categories such as delays or cancellations, mission interruptions or additional duties for the crew. This module must take into account allowable tolerances such as the operation of an aircraft with non-critical non-operational equipment and the limitations resulting from the loss of a non-essential function. These tolerances and limitations are, for example, determined by the MEL which specifies, in particular, for each function whether or not it is indispensable to be able to fly, in which period it must be rehabilitated and if there are operational procedures or maintenance requirements associated with its failure. Such procedures are for example the deactivation of the system or the inspection before each flight. A malfunction may reduce the operational capabilities of the aircraft, such as prohibiting automatic landing or requiring repair within a specified time, imposing specific operating or maintenance procedures. The impacts on the execution of the mission may be to prevent the aircraft from making the next flight, causing a delay or increasing the cost of operation. The operational evaluation module offers the following service, getFlightlmpacts (failure, vo / 1, vol2): a list of impacts that returns the impact of the occurrence of a failure, the failure occurring during volt volt and the impact being evaluated on flight vol2 assuming that the repair is not performed between the two flights. The probability of failure on the flight can be obtained by the getFailureOccurrence service of the functional evaluation module.

  The decision support module presents the user with the results of the previously described modules in order to help him decide on the actions to be carried out. At a minimum, the functional and operational impacts of each detected failure or degradation will be presented. Other derived information may also be useful as a descending priority ranking of detected anomalies, an estimate of the operational reliability of the aircraft for all planned missions and the operational impact of a hypothetical maintenance decision. These queries are based on the cascading use of services offered by the different parts of the described system.

  Figure 5 illustrates cascading calls made when a user asks the system for the probability of being able to perform all scheduled flights.

  As shown, the user transmits a request 500 through the decision support module to obtain the list of planned or current failures. This request, addressed to the functional evaluation module, causes the execution of a request 505 to obtain from the prognostic module the list of detected impairments. Simultaneously, or thereafter, a request 510 is executed to obtain the list of failed components of the diagnostic module. A series of requests 515 is then executed to determine the probability of a failure occurring during a given flight. The failures here are those that can result from the detected degradations. The request is executed for each detected fault, for each detected degradation and for each planned flight. The request to determine the probability of a failure occurring during a given flight is transmitted by the decision support module to the functional evaluation module. To execute this request, the functional evaluation module itself transmits a request to the contextualization module to obtain the probability of the proper functioning of the component during a given flight. The contextualization module in turn transmits a similar request 525 to obtain the probability of the proper functioning of the component according to its use. The result is converted according to planned flights. The decision support module then transmits a request 530 to the operational evaluation module to determine the impacts of the failed components and degradations detected on the planned flights.

  The decision support system according to the invention can be implemented in different ways. In particular, the system can be implemented entirely in the vehicle or partly in the vehicle and partly in one or more fixed stations, for example ground stations. FIG. 6 illustrates a first example of implementation in which the decision support system 600 is fully implemented in the vehicle, that is to say that the decision support system 600 is autonomous even if it can be linked punctually to one or more fixed stations.

  In particular, the decision support system 600 may be linked punctually to one or more fixed stations to access information such as fault trees and degradation models used for the prognosis which can then be stored locally.

  As shown, the decision support system 600 is connected to the communication bus 415 itself connected, in particular, to the various monitored components 305-1 to 305-n as well as the sensors 405 and the processing systems 410. data. The decision support system 600 includes a central CMS system 315, connected to the communication bus 415, which provides complete and accurate diagnostic results. The decision support system 600 also includes a central ACMS system 420, connected to the communication bus 415, which determines prognostic data. The prognostic data is here stored in a database 425 before being used by 430-1 to 430-m trend analysis and probability probability calculation functions. The prognostic data including the determined probabilities of failure are transmitted to the centralized prediction interface 435 and then adapted into the contextualization module 605 according to the planned flights. The planned flights are here stored in a database 610. Alternatively, the information concerning planned flights can be accessed via a local or remote network. The database 610 is also used here to memorize failure patterns as well as tolerances and limitations. The prognostic data relating to the planned flights are then transmitted to the functional evaluation module 615 to determine the consequences of the observed impairments. The data from the diagnostic and functional evaluation modules 315 and 615, respectively, are then transmitted to the operational evaluation module 620 which in turn transmits the data necessary for decision support to the help module. Decision 625.

  FIG. 7 illustrates a second exemplary implementation in which the decision support system comprises a first part 700 implemented in the vehicle and a second part 705 implemented in one or more fixed stations.

  According to this embodiment, a first part of the method is performed locally in the vehicle while another part is performed in one or more fixed stations. For reasons of cost and availability of the means of communication between the vehicle and the fixed stations, a storage of the results of diagnosis and prognosis in the fixed stations may be necessary, in particular to avoid that the analysis functions make numerous calls to the services of diagnostic and prognostic modules embedded in the vehicles. To this end, a database 710 is used to store the data from the diagnostic module 315 and the centralized forecast interface 435. When the vehicle is close to the fixed stations implementing the other part of the help method at the decision, the data stored in the database 710 are transferred to these fixed stations. The transfer may in particular be carried out by network, wired or wireless, or by the physical displacement of a memory medium containing these data, for example a memory card, a hard disk or a DVD-type disk. This second embodiment thus makes it possible to limit the processing of data in the vehicle and to centralize the flight data, the failure models and the tolerances and limitations in one or more fixed stations, that is to say minimize data updates on the aircraft and better align with the vehicle operating organization, including airlines for which maintenance decisions are made on the ground in a maintenance control center called MCC ( acronym for Maintenance Control Center in English terminology). The MCC decides on the maintenance actions to be carried out and transmits its orders to the maintenance teams working on the aircraft. It is possible to move other elements of part 700 to part 705 and vice versa. In particular, the centralized prognostic interface and at least a portion of at least some trend analysis functions can be implemented in the fixed stations so as to capitalize the trend analysis experience according to a history of several vehicles and / or to use a larger computing power to refine the trend estimation and estimate the probability of failure.

  In addition, the operational evaluation module can integrate functions taking into account operating costs to optimize maintenance operations. Naturally, to meet specific needs, a person skilled in the field of the invention may apply modifications in the foregoing description.

Claims (7)

  A decision-support method for maintenance operations of vehicle components during the planned operation of said vehicle, said planned operation comprising at least one operating period, this method being characterized in that comprises the following steps, - analyzing (130) the degradation of at least one of said components and transmitting at least one data item related to the result of said analysis; - Receiving said at least one data item and evaluating (125) the functional consequences on said vehicle of said degradation analyzed, from said at least one received data, according to said planned operation of said vehicle; and, - determining (110) an operational index of said vehicle for said at least one period according to said evaluated functional consequences.   2. Method according to claim 1 characterized in that it further comprises a step of determining (120) a functional state of at least one of said components, said step of determining an operational index of said vehicle for said at least one of said components; at least one period being based on said evaluated functional consequences and on said functional state.   3. Method according to claim 1 or claim 2 characterized in that said step of analyzing the degradation of at least one of said components comprises a step of estimating the degradation trend of said at least one of said components and of evaluating a probability of failure of said at least one of said components.   4. Method according to any one of the preceding claims characterized in that said step of evaluating the functional consequences on said vehicle of said degradation analyzed according to said planned operation of said vehicle uses a predetermined failure model.   5. Device comprising means adapted to the implementation of each of the steps of the method according to any one of the preceding claims.   6. Computer program comprising instructions adapted to the implementation of each of the steps of the method according to any one of claims 1 to 4.   7. A decision-aid device for maintenance operations of components of a vehicle during the planned operation of said vehicle, said planned operation comprising at least one operating period, this device being characterized in that it comprises the following means - means (310) for determining a functional state of at least one of said components; means (405, 410) for determining the degradation of at least one of said components; means (430) for analyzing said determined degradation; and, means for transmitting said functional state and said analysis of said degradation. 12. Device according to the preceding claim characterized in that said means for analyzing the degradation of at least one of said components comprises means for estimating the degradation trend of said at least one of said components and for evaluating a probability of failure of said at least one of said components. one of said components. 13. Decision support device for maintenance operations of components of a vehicle during the planned operation of said vehicle, said planned operation comprising at least one operating period, this device being characterized in that it comprises the following means, - means for receiving a functional state of at least one of said components and a degradation analysis of at least one of said components; - means (615) for evaluating functional consequences on said vehicle of said degradation analyzed. according to said planned operation of said vehicle; and, means (620) for determining an operational index of said vehicle for said at least one period according to said functional consequences evaluated and according to said functional state of said at least one of said components. 10. Device according to the preceding claim characterized in that said means for evaluating functional consequences comprise means for estimating the degradation trend of said at least one of said components and for evaluating a probability of failure of said at least one of said components.